marian/CHANGELOG.md

Changelog

All notable changes to this project will be documented in this file.

The format is based on Keep a Changelog and this project adheres to Semantic Versioning.

[Unreleased]

[1.6.0] - 2018-08-08

Added

• Faster training (20-30%) by optimizing gradient popagation of biases
• Returning Moses-style hard alignments during decoding single models, ensembles and n-best lists
• Hard alignment extraction strategy taking source words that have the attention value greater than the threshold
• Refactored sync sgd for easier communication and integration with NCCL
• Smaller memory-overhead for sync-sgd
• NCCL integration (version 2.2.13)
• New binary format for saving/load of models, can be used with *.bin extension (can be memory mapped)
• Memory-mapping of graphs for inferece with ExpressionGraph::mmap(const void* ptr) function. (assumes *.bin model is mapped or in buffer)
• Added SRU (--dec-cell sru) and ReLU (--dec-cell relu) cells to inventory of RNN cells.
• RNN auto-regression layers in transformer (--transformer-decoder-autreg rnn), work with gru, lstm, tanh, relu, sru cells.
• Recurrently stacked layers in transformer (--transformer-tied-layers 1 1 1 2 2 2 means 6 layers with 1-3 and 4-6 tied parameters, two groups of parameters)

Fixed

• A couple of bugs in "selection" (transpose, shift, cols, rows) operators during back-prob for a very specific case: one of the operators is the first operator after a branch, in that case gradient propgation might be interrupted. This did not affect any of the existing models as such a case was not present, but might have caused future models to not train properly.
• Bug in mini-batch-fit, tied embeddings would result in identical embeddings in fake source and target batch. Caused under-estimation of memory usage and re-allocation.
• Seamless training continuation with exponential smoothing

[1.5.0] - 2018-06-17

Added

• Average Attention Networks for Transformer model
• 16-bit matrix multiplication on CPU
• Memoization for constant nodes for decoding
• Autotuning for decoding

Fixed

• GPU decoding optimizations, about 2x faster decoding of transformer models
• Multi-node MPI-based training on GPUs

[1.4.0] - 2018-03-13

Added

• Data weighting with --data-weighting at sentence or word level
• Persistent SQLite3 corpus storage with --sqlite file.db
• Experimental multi-node asynchronous training
• Restoring optimizer and training parameters such as learning rate, validation results, etc.
• Experimental multi-CPU training/translation/scoring with --cpu-threads=N
• Restoring corpus iteration after training is restarted
• N-best-list scoring in marian-scorer

Fixed

• Deterministic data shuffling with specific seed for SQLite3 corpus storage
• Mini-batch fitting with binary search for faster fitting
• Better batch packing due to sorting

[1.3.1] - 2018-02-04

Fixed

• Missing final validation when done with training
• Differing summaries for marian-scorer when used with multiple GPUs

[1.3.0] - 2018-01-24

Added

• SQLite3 based corpus storage for on-disk shuffling etc. with --sqlite
• Asynchronous maxi-batch preloading
• Using transpose in SGEMM to tie embeddings in output layer

[1.2.1] - 2018-01-19

Fixed

• Use valid-mini-batch size during validation with "translation" instead of mini-batch
• Normalize gradients with multi-gpu synchronous SGD
• Fix divergence between saved models and validated models in asynchronous SGD

[1.2.0] - 2018-01-13

Added

• Option --pretrained-model to be used for network weights initialization with a pretrained model
• Version number saved in the model file
• CMake option -DCOMPILE_SERVER=ON
• Right-to-left training, scoring, decoding with --right-left

Fixed

• Fixed marian-server compilation with Boost 1.66
• Fixed compilation on g++-4.8.4
• Fixed compilation without marian-server if openssl is not available

[1.1.3] - 2017-12-06

Added

• Added back gradient-dropping

Fixed

• Fixed parameters initialization for --tied-embeddings during translation

[1.1.2] - 2017-12-05

Fixed

• Fixed ensembling with language model and batched decoding
• Fixed attention reduction kernel with large matrices (added missing syncthreads()), which should fix stability with large batches and beam-size during batched decoding.

[1.1.1] - 2017-11-30

Added

• Option --max-length-crop to be used together with --max-length N to crop sentences to length N rather than omitting them.
• Experimental model with convolution over input characters

Fixed

• Fixed a number of bugs for vocabulary and directory handling

[1.1.0] - 2017-11-21

Added

• Batched translation for all model types, significant translation speed-up
• Batched translation during validation with translation
• --maxi-batch-sort option for marian-decoder
• Support for CUBLAS_TENSOR_OP_MATH mode for cublas in cuda 9.0
• The "marian-vocab" tool to create vocabularies

[1.0.0] - 2017-11-13

Added

• Multi-gpu validation, scorer and in-training translation
• summary-mode for scorer
• New "transformer" model based on Attention is all you need
• Options specific for the transformer model
• Linear learning rate warmup with and without initial value
• Cyclic learning rate warmup
• More options for learning rate decay, including: optimizer history reset, repeated warmup
• Continuous inverted square root decay of learning (--lr-decay-inv-sqrt) rate based on number of updates
• Exposed optimizer parameters (e.g. momentum etc. for Adam)
• Version of deep RNN-based models compatible with Nematus (--type nematus)
• Synchronous SGD training for multi-gpu (enable with --sync-sgd)
• Dynamic construction of complex models with different encoders and decoders, currently only available through the C++ API
• Option --quiet to suppress output to stderr
• Option to choose different variants of optimization criterion: mean cross-entropy, perplexity, cross-entropy sum
• In-process translation for validation, uses the same memory as training
• Label Smoothing
• CHANGELOG.md
• CONTRIBUTING.md
• Swish activation function default for Transformer (https://arxiv.org/pdf/1710.05941.pdf)

Changed

• Changed shape organization to follow numpy.
• Changed option --moving-average to --exponential-smoothing and inverted formula to s_t = (1 - \alpha) * s_{t-1} + \alpha * x_t, \alpha is now 1-e4 by default
• Got rid of thrust for compile-time mathematical expressions
• Changed boolean option --normalize to --normalize [arg=1] (=0). New behaviour is backwards-compatible and can also be specified as --normalize=0.6
• Renamed "s2s" binary to "marian-decoder"
• Renamed "rescorer" binary to "marian-scorer"
• Renamed "server" binary to "marian-server"
• Renamed option name --dynamic-batching to --mini-batch-fit
• Unified cross-entropy-based validation, supports now perplexity and other CE
• Changed --normalize (bool) to --normalize (float)arg, allow to change length normalization weight as score / pow(length, arg).

Removed

• Temporarily removed gradient dropping (--drop-rate X) until refactoring.